Three building blocks that bring analytics from the tech business to the insights business

By Lori C. Bieda, VP Customer Intelligence, SAS

The evolution of analytical teams shows the importance of integrating insight and data across the board – no matter where you sit. Consider these points of organizational progress when developing or revitalizing your analytics infrastructure:

The integration of the PhDs

Analytics teams started with a group of people who mined information warehouses and later, marketing databases. They ran standard queries, extracted information and passed it on to business users. Before long, statisticians and PhDs appeared and were dropped into data groups with the logic that if someone could lead analysts, perhaps they could also speak the same language as statisticians.

Some statisticians and PhDs were even rolled into marketing, risk and product teams, but their skill was largely misunderstood, their value untapped. They were the “data guys.” Best if we left them alone.

The centralization of insights

Later on, many analytical groups opened their arms and welcomed more people into their fold: a few database marketers, some market researchers. Some of them went kicking and screaming, claiming they were different or didn’t belong – that their homes were really with the marketers or the strategists; that the kind of insights they brought were unique. They all spoke different languages, but they were heaped together into a diverse group and we implored them to speak the same language. The truth was, each of these analytical experts was more like their counterparts than they were willing to admit. They were all in the insights business, each of them was just using different instruments to extract their gold.

More and more, the collection of business insights showed up under a single leader. The problem was they were just that – a collection of individual experts with varied disciplines and no clear, common thread that bound them. They operated (and most still do operate) in analytical silos, much like how businesses operate with multiple product lines and leaders. They just all answered to the same leader, and sat around management tables checking one another out with a reserved curiosity.

So, while the data may have been consciously pooled in one place, and a leader anointed head of it, getting to the business analytics – the golden nuggets of insight that lie resident in the company’s data banks and data brains – was still increasingly difficult, like getting our arms around water.

The leaders of analytical teams

The leader, the person managing these analytical communities, was usually a hybrid of some sort. Some were seasoned analytical types, others were CRM or technology people; sometimes marketers or even general business people took the reins. We never really understood who they were, or what we were looking for exactly when management created their job descriptions. We just hoped they knew something about data and could explain it in plain terms to everyone else.

As the analytics teams took shape, the analysts worked in back rooms cranking through data. They would stumble upon insights that could help the business, but they had limited means for getting their secrets out. They were highly dependent on the people asking them for the data. The analysts had to hope the requestors knew what they were asking for, knew how the information could be used, and considered the problem with a broad enough lens to ensure the analysis could deliver sufficient value back to the business. Sometimes the requests were so broad that it was unclear what they were asking for. Other times, the requests were so narrow that it begged the question, Why have a database and insights team at all?

But as the years passed, central analytics teams began to form. Large insight and analytics teams appeared in data-rich industries, such as financial services and telecommunications, to name a few. This collection of analytically minded people signaled change (analyst contributions were being valued differently), but it also signaled a challenge: It was difficult enough to make sense of one business problem, but how would we leverage our full range of insights to make sense of what was happening to the business overall?

Analysts would stumble upon insights that could help the business but had limited means of getting their secrets out. They were highly dependent on the people asking for the data knowing what they were asking for, knowing how the information could be used, and considering the problem with a broad enough lens to ensure the analysis could deliver sufficient value back to the business.

But as the years passed, central analytics teams began to form. Large insight and analytics teams appeared in data-rich industries, such as financial services and telecommunications, to name a few. This collection of analytically minded people signaled change (analyst contributions were being valued differently), but it also signaled a challenge: It was difficult enough to make sense of one business problem, but how would we leverage our full range of insights to make sense of what was happening to the business overall?

From service provider to business driver

The vast majority of companies are trying to compete by knowing and serving customers better than the competition – and are dealing with ever-increasing amounts and complexity of data along the way. Incremental competitive gains are just keeping companies on par with each other -- the real gold lies in applying analytics mastery to unearth more customer insight than anyone else. And this requires a phenomenal translation layer to get at the insights.

It comes down to this: There are shades in the data that require a skilled eye to see. It is no longer enough for analysts to stay in their silos of expertise, cranking out back-end marketing campaign analysis without consideration for broader market forces; the cannibalization impact one campaign has over another; what the voice of the customer is telling us; how the overall market is changing or converging; or the impact of the brand on our overall results.

If the responsibility for this translation is relegated to others in the business (marketers, product teams, finance teams, strategists or upper management), those others will never be in a position where they are close enough to the data to interpret it correctly across the business. The analytics community must “translate” for the good of the business.

Because of their heritage, many analytical teams have grown up in the service provider space, their success defined by delivering what they were asked to do, on time and accurately. Yet as data continues to become increasingly central to organizations, and a key business enabler, the role the analytics community plays needs to evolve. They need to – and most certainly can -- become business drivers.